In [72]:
import matplotlib.pyplot as plt
plt.imshow(test_image);
Notes about the instance size and kernel setup: this notebook has been tested on
Python 3 (Data Science) kernelml.t3.medium Sagemaker notebook instanceWe'll use a sample dataset called CIFAR to simulate the challenges Scones Unlimited are facing in Image Classification. In order to start working with CIFAR we'll need to:
In other words, we're going to do some simple ETL!
In the cell below, define a function extract_cifar_data that extracts python version of the CIFAR-100 dataset. The CIFAR dataaset is open source and generously hosted by the University of Toronto at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz
import requests
def extract_cifar_data(url, filename="cifar.tar.gz"):
"""A function for extracting the CIFAR-100 dataset and storing it as a gzipped file
Arguments:
url -- the URL where the dataset is hosted
filename -- the full path where the dataset will be written
"""
r = requests.get(url)
with open(filename, "wb") as file_context:
file_context.write(r.content)
return
Let's test it out! Run the following cell and check whether a new file cifar.tar.gz is created in the file explorer.
extract_cifar_data("https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz")
Clearly, distributing the data as a gzipped archive makes sense for the hosting service! It saves on bandwidth, storage, and it's a widely-used archive format. In fact, it's so widely used that the Python community ships a utility for working with them, tarfile, as part of its Standard Library. Execute the following cell to decompress your extracted dataset:
import tarfile
with tarfile.open("cifar.tar.gz", "r:gz") as tar:
tar.extractall()
A new folder cifar-100-python should be created, containing meta, test, and train files. These files are pickles and the CIFAR homepage provides a simple script that can be used to load them. We've adapted the script below for you to run:
import pickle
with open("./cifar-100-python/meta", "rb") as f:
dataset_meta = pickle.load(f, encoding='bytes')
with open("./cifar-100-python/test", "rb") as f:
dataset_test = pickle.load(f, encoding='bytes')
with open("./cifar-100-python/train", "rb") as f:
dataset_train = pickle.load(f, encoding='bytes')
# Feel free to explore the datasets
dataset_train.keys()
dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])
type(dataset_train)
dict
As documented on the homepage, b'data' contains rows of 3073 unsigned integers, representing three channels (red, green, and blue) for one 32x32 pixel image per row.
32*32*3
3072
For a simple gut-check, let's transform one of our images. Each 1024 items in a row is a channel (red, green, then blue). Each 32 items in the channel are a row in the 32x32 image. Using python, we can stack these channels into a 32x32x3 array, and save it as a PNG file:
import numpy as np
# Each 1024 in a row is a channel (red, green, then blue)
row = dataset_train[b'data'][0]
red, green, blue = row[0:1024], row[1024:2048], row[2048:]
# Each 32 items in the channel are a row in the 32x32 image
red = red.reshape(32,32)
green = green.reshape(32,32)
blue = blue.reshape(32,32)
# Combine the channels into a 32x32x3 image!
combined = np.dstack((red,green,blue))
For a more concise version, consider the following:
# All in one:
test_image = np.dstack((
row[0:1024].reshape(32,32),
row[1024:2048].reshape(32,32),
row[2048:].reshape(32,32)
))
%matplotlib inline
import matplotlib.pyplot as plt
plt.imshow(test_image);
Looks like a cow! Let's check the label. dataset_meta contains label names in order, and dataset_train has a list of labels for each row.
dataset_train[b'fine_labels'][0]
19
Our image has a label of 19, so let's see what the 19th item is in the list of label names.
print(dataset_meta[b'fine_label_names'][19])
b'cattle'
Ok! 'cattle' sounds about right. By the way, using the previous two lines we can do:
n = 0
print(dataset_meta[b'fine_label_names'][dataset_train[b'fine_labels'][n]])
b'cattle'
Now we know how to check labels, is there a way that we can also check file names? dataset_train also contains a b'filenames' key. Let's see what we have here:
print(dataset_train[b'filenames'][0])
b'bos_taurus_s_000507.png'
"Taurus" is the name of a subspecies of cattle, so this looks like a pretty reasonable filename. To save an image we can also do:
plt.imsave("file.png", test_image)
Your new PNG file should now appear in the file explorer -- go ahead and pop it open to see!
Now that you know how to reshape the images, save them as files, and capture their filenames and labels, let's just capture all the bicycles and motorcycles and save them. Scones Unlimited can use a model that tells these apart to route delivery drivers automatically.
In the following cell, identify the label numbers for Bicycles and Motorcycles:
import pandas as pd
# Todo: Filter the dataset_train and dataset_meta objects to find the label numbers for Bicycle and Motorcycles
# Extract fine label names
filtered_labels = {x:idx for idx, x in enumerate(dataset_meta[b'fine_label_names']) if x==b'bicycle' or x==b'motorcycle'}
Good job! We only need objects with label 8 and 48 -- this drastically simplifies our handling of the data! Below we construct a dataframe for you, and you can safely drop the rows that don't contain observations about bicycles and motorcycles. Fill in the missing lines below to drop all other rows:
print(type(dataset_train))
print(dataset_train.keys())
print(type(dataset_test))
print(dataset_test.keys())
<class 'dict'> dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data']) <class 'dict'> dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])
#Construct the dataframe
df_train = pd.DataFrame({
"filenames": dataset_train[b'filenames'],
"labels": dataset_train[b'fine_labels'],
"row": range(len(dataset_train[b'filenames']))
})
# Drop all rows from df_train where label is not 8 or 48
df_train = df_train[df_train["labels"].isin([8, 48])]
# Decode df_train.filenames so they are regular strings
df_train["filenames"] = df_train["filenames"].apply(
lambda x: x.decode("utf-8")
)
df_test = pd.DataFrame({
"filenames": dataset_test[b'filenames'],
"labels": dataset_test[b'fine_labels'],
"row": range(len(dataset_test[b'filenames']))
})
# Drop all rows from df_test where label is not 8 or 48
df_test = df_test[df_test["labels"].isin([8, 48])]
# Decode df_test.filenames so they are regular strings
df_test["filenames"] = df_test["filenames"].apply(
lambda x: x.decode("utf-8")
)
Now that the data is filtered for just our classes, we can save all our images.
!mkdir ./train
!mkdir ./test
In the previous sections we introduced you to several key snippets of code:
dataset_train[b'data'][0]
import numpy as np
np.dstack((
row[0:1024].reshape(32,32),
row[1024:2048].reshape(32,32),
row[2048:].reshape(32,32)
))
matplotlib utility for saving imagesplt.imsave(path+row['filenames'], target)
Compose these together into a function that saves all the images into the ./test and ./train directories. Use the comments in the body of the save_images function below to guide your construction of the function:
import os
def save_images(df, data, directory):
#Grab the image data in row-major form
for index, row in df.iterrows():
img = data[b'data'][index]
# Consolidated stacking/reshaping from earlier
target = np.dstack((
img[0:1024].reshape(32, 32), # Red channel
img[1024:2048].reshape(32, 32), # Green channel
img[2048:].reshape(32, 32) # Blue channel
))
# Save the image
plt.imsave(os.path.join(directory, row['filenames']), target)
# Return any signal data you want for debugging
return df.head()
## TODO: save ALL images using the save_images function
save_images(df_train, dataset_train, './train')
save_images(df_test, dataset_test, './test')
| filenames | labels | row | |
|---|---|---|---|
| 27 | safety_bike_s_000390.png | 8 | 27 |
| 28 | bike_s_000658.png | 8 | 28 |
| 116 | velocipede_s_001744.png | 8 | 116 |
| 161 | bike_s_000643.png | 8 | 161 |
| 319 | ordinary_bicycle_s_000437.png | 8 | 319 |
Now we can load the data into S3.
Using the sagemaker SDK grab the current region, execution role, and bucket.
import sagemaker
import boto3
from sagemaker import get_execution_role
# Create a SageMaker session
session = sagemaker.Session()
# Get the default bucket
bucket = session.default_bucket()
print("Default Bucket: {}".format(bucket))
# Get the current AWS region
region = boto3.Session().region_name
print("AWS Region: {}".format(region))
# Get the SageMaker execution role
role = get_execution_role()
print("RoleArn: {}".format(role))
Default Bucket: sagemaker-us-east-1-933188268477 AWS Region: us-east-1 RoleArn: arn:aws:iam::933188268477:role/service-role/AmazonSageMaker-ExecutionRole-20240719T112975
With this data we can easily sync your data up into S3!
import os
os.environ["DEFAULT_S3_BUCKET"] = bucket
!aws s3 sync ./train s3://${DEFAULT_S3_BUCKET}/train/
!aws s3 sync ./test s3://${DEFAULT_S3_BUCKET}/test/
upload: train/bicycle_s_000017.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000017.png upload: train/bicycle_s_000071.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000071.png upload: train/bicycle_s_000038.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000038.png upload: train/bicycle_s_000021.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000021.png upload: train/bicycle_s_000137.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000137.png upload: train/bicycle_s_000035.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000035.png upload: train/bicycle_s_000051.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000051.png upload: train/bicycle_s_000043.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000043.png upload: train/bicycle_s_000039.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000039.png upload: train/bicycle_s_000066.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000066.png upload: train/bicycle_s_000231.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000231.png upload: train/bicycle_s_000159.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000159.png upload: train/bicycle_s_000156.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000156.png upload: train/bicycle_s_000180.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000180.png upload: train/bicycle_s_000099.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000099.png upload: train/bicycle_s_000147.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000147.png upload: train/bicycle_s_000235.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000235.png upload: train/bicycle_s_000124.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000124.png upload: train/bicycle_s_000279.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000279.png upload: train/bicycle_s_000243.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000243.png upload: train/bicycle_s_000282.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000282.png upload: train/bicycle_s_000369.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000369.png upload: train/bicycle_s_000314.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000314.png upload: train/bicycle_s_000371.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000371.png upload: train/bicycle_s_000392.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000392.png upload: train/bicycle_s_000399.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000399.png upload: train/bicycle_s_000149.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000149.png upload: train/bicycle_s_000408.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000408.png upload: train/bicycle_s_000396.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000396.png upload: train/bicycle_s_000435.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000435.png upload: train/bicycle_s_000467.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000467.png upload: train/bicycle_s_000437.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000437.png upload: train/bicycle_s_000491.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000491.png upload: train/bicycle_s_000522.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000522.png upload: train/bicycle_s_000463.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000463.png upload: train/bicycle_s_000546.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000546.png upload: train/bicycle_s_000561.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000561.png upload: train/bicycle_s_000569.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000569.png upload: train/bicycle_s_000536.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000536.png upload: train/bicycle_s_000537.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000537.png upload: train/bicycle_s_000668.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000668.png upload: train/bicycle_s_000753.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000753.png upload: train/bicycle_s_000774.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000774.png upload: train/bicycle_s_000667.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000667.png upload: train/bicycle_s_000759.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000759.png upload: train/bicycle_s_000782.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000782.png upload: train/bicycle_s_000775.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000775.png upload: train/bicycle_s_000829.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000829.png upload: train/bicycle_s_000723.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000723.png upload: train/bicycle_s_000778.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000778.png upload: train/bicycle_s_000781.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000781.png upload: train/bicycle_s_000951.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000951.png upload: train/bicycle_s_000861.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000861.png upload: train/bicycle_s_000785.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000785.png upload: train/bicycle_s_000978.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000978.png upload: train/bicycle_s_000822.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000822.png upload: train/bicycle_s_000996.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000996.png upload: train/bicycle_s_001168.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001168.png upload: train/bicycle_s_001111.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001111.png upload: train/bicycle_s_001174.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001174.png upload: train/bicycle_s_001002.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001002.png upload: train/bicycle_s_001228.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001228.png upload: train/bicycle_s_001205.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001205.png upload: train/bicycle_s_000986.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_000986.png upload: train/bicycle_s_001245.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001245.png upload: train/bicycle_s_001247.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001247.png upload: train/bicycle_s_001338.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001338.png upload: train/bicycle_s_001348.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001348.png upload: train/bicycle_s_001388.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001388.png upload: train/bicycle_s_001402.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001402.png upload: train/bicycle_s_001409.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001409.png upload: train/bicycle_s_001411.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001411.png upload: train/bicycle_s_001448.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001448.png upload: train/bicycle_s_001452.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001452.png upload: train/bicycle_s_001203.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001203.png upload: train/bicycle_s_001470.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001470.png upload: train/bicycle_s_001554.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001554.png upload: train/bicycle_s_001569.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001569.png upload: train/bicycle_s_001583.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001583.png upload: train/bicycle_s_001642.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001642.png upload: train/bicycle_s_001679.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001679.png upload: train/bicycle_s_001688.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001688.png upload: train/bicycle_s_001681.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001681.png upload: train/bicycle_s_001687.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001687.png upload: train/bicycle_s_001673.png to s3://sagemaker-us-east-1-933188268477/train/bicycle_s_001673.png upload: train/bicycle_s_001693.png to 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s3://sagemaker-us-east-1-933188268477/test/safety_bicycle_s_000280.png upload: test/safety_bicycle_s_000235.png to s3://sagemaker-us-east-1-933188268477/test/safety_bicycle_s_000235.png upload: test/safety_bike_s_000488.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_000488.png upload: test/safety_bike_s_000540.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_000540.png upload: test/safety_bike_s_000699.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_000699.png upload: test/safety_bike_s_001065.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001065.png upload: test/safety_bike_s_000791.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_000791.png upload: test/safety_bike_s_000541.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_000541.png upload: test/safety_bike_s_001132.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001132.png upload: test/safety_bike_s_001088.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001088.png upload: test/safety_bike_s_001087.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001087.png upload: test/safety_bike_s_001253.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001253.png upload: test/safety_bike_s_001481.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001481.png upload: test/safety_bike_s_001690.png to s3://sagemaker-us-east-1-933188268477/test/safety_bike_s_001690.png upload: test/velocipede_s_001201.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001201.png upload: test/velocipede_s_000369.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000369.png upload: test/velocipede_s_000292.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000292.png upload: test/trail_bike_s_000073.png to s3://sagemaker-us-east-1-933188268477/test/trail_bike_s_000073.png upload: test/velocipede_s_000041.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000041.png upload: test/velocipede_s_000001.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000001.png upload: test/velocipede_s_001277.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001277.png upload: test/velocipede_s_001232.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001232.png upload: test/velocipede_s_000106.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000106.png upload: test/velocipede_s_001466.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001466.png upload: test/velocipede_s_001355.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001355.png upload: test/velocipede_s_001335.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001335.png upload: test/velocipede_s_001379.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001379.png upload: test/velocipede_s_001744.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001744.png upload: test/velocipede_s_000863.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_000863.png upload: test/velocipede_s_001790.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001790.png upload: test/velocipede_s_001699.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001699.png upload: test/velocipede_s_001633.png to s3://sagemaker-us-east-1-933188268477/test/velocipede_s_001633.png
And that's it! You can check the bucket and verify that the items were uploaded.
For Image Classification, Sagemaker also expects metadata e.g. in the form of TSV files with labels and filepaths. We can generate these using our Pandas DataFrames from earlier:
def to_metadata_file(df, prefix):
df["s3_path"] = df["filenames"]
df["labels"] = df["labels"].apply(lambda x: 0 if x==8 else 1)
return df[["row", "labels", "s3_path"]].to_csv(
f"{prefix}.lst", sep="\t", index=False, header=False
)
to_metadata_file(df_train.copy(), "train")
to_metadata_file(df_test.copy(), "test")
We can also upload our manifest files:
import boto3
# Upload files
boto3.Session().resource('s3').Bucket(
bucket).Object('train.lst').upload_file('./train.lst')
boto3.Session().resource('s3').Bucket(
bucket).Object('test.lst').upload_file('./test.lst')
Using the bucket and region info we can get the latest prebuilt container to run our training job, and define an output location on our s3 bucket for the model. Use the image_uris function from the SageMaker SDK to retrieve the latest image-classification image below:
# Use the image_uris function to retrieve the latest 'image-classification' image
from sagemaker import image_uris
algo_image =image_uris.retrieve(framework='image-classification', region=region)
s3_output_location = f"s3://{bucket}/models/image_model"
INFO:sagemaker.image_uris:Same images used for training and inference. Defaulting to image scope: inference. INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1. INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.
We're ready to create an estimator! Create an estimator img_classifier_model that uses one instance of ml.g4dn.xlarge. Ensure that y ou use the output location we defined above - we'll be referring to that later!
import sagemaker
img_classifier_model=sagemaker.estimator.Estimator(
image_uri=algo_image, # The container image URI for the image classification algorithm
role=role, # The IAM role to use for training
instance_count=1, # Number of instances to use for training
instance_type='ml.p3.2xlarge', # Type of instance to use for training
output_path=s3_output_location, # Where to save the model artifacts
sagemaker_session=session # The current SageMaker session
)
We can also set a few key hyperparameters and define the inputs for our model:
img_classifier_model.set_hyperparameters(
image_shape='3,32,32', # Input image dimensions (Format: 'num_channels, height, width')
num_classes=2, # Number of output classes (2 classes: 0 and 1 for '8' and '48' class labels)
num_training_samples=1000, # Number of training examples in the input dataset
augmentation_type= "crop_color_transform",# Data Augmentation type
early_stopping=True, # Provide early stopping criteria to avoid overfitting
epochs = 27, # Maximum number of training epochs
optimizer='adam',
learning_rate=0.001
)
The image-classification image uses four input channels with very specific input parameters. For convenience, we've provided them below:
from sagemaker.debugger import Rule, rule_configs
from sagemaker.session import TrainingInput
model_inputs = {
"train": sagemaker.inputs.TrainingInput(
s3_data=f"s3://{bucket}/train/",
content_type="application/x-image"
),
"validation": sagemaker.inputs.TrainingInput(
s3_data=f"s3://{bucket}/test/",
content_type="application/x-image"
),
"train_lst": sagemaker.inputs.TrainingInput(
s3_data=f"s3://{bucket}/train.lst",
content_type="application/x-image"
),
"validation_lst": sagemaker.inputs.TrainingInput(
s3_data=f"s3://{bucket}/test.lst",
content_type="application/x-image"
)
}
Great, now we can train the model using the model_inputs. In the cell below, call the fit method on our model,:
## TODO: train your model
# Call the fit method on the img_classifier_model to start training
img_classifier_model.fit(model_inputs)
INFO:sagemaker:Creating training-job with name: image-classification-2024-08-23-14-06-45-309
2024-08-23 14:06:45 Starting - Starting the training job... 2024-08-23 14:07:09 Pending - Training job waiting for capacity... 2024-08-23 14:07:34 Pending - Preparing the instances for training... 2024-08-23 14:08:10 Downloading - Downloading input data... 2024-08-23 14:08:30 Downloading - Downloading the training image........................... 2024-08-23 14:12:58 Training - Training image download completed. Training in progress...Docker entrypoint called with argument(s): train Running default environment configuration script Nvidia gpu devices, drivers and cuda toolkit versions (only available on hosts with GPU): Fri Aug 23 14:13:21 2024 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.183.01 Driver Version: 535.183.01 CUDA Version: 12.2 | |-----------------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+======================+======================| | 0 Tesla V100-SXM2-16GB On | 00000000:00:1E.0 Off | 0 | | N/A 31C P0 23W / 300W | 0MiB / 16384MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | No running processes found | +---------------------------------------------------------------------------------------+ Checking for nvidia driver and cuda compatibility. CUDA Compatibility driver provided. Proceeding with compatibility check between driver, cuda-toolkit and cuda-compat. Detected cuda-toolkit version: 11.1. Detected cuda-compat version: 455.32.00. Detected Nvidia driver version: 535.183.01. Nvidia driver compatible with cuda-toolkit. Disabling cuda-compat. /opt/amazon/lib/python3.8/site-packages/mxnet/model.py:97: SyntaxWarning: "is" with a literal. Did you mean "=="? if num_device is 1 and 'dist' not in kvstore: [08/23/2024 14:13:25 INFO 140424560326464] Reading default configuration from /opt/amazon/lib/python3.8/site-packages/image_classification/default-input.json: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,224,224', 'precision_dtype': 'float32'} [08/23/2024 14:13:25 INFO 140424560326464] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'augmentation_type': 'crop_color_transform', 'early_stopping': 'True', 'epochs': '27', 'image_shape': '3,32,32', 'learning_rate': '0.001', 'num_classes': '2', 'num_training_samples': '1000', 'optimizer': 'adam'} [08/23/2024 14:13:25 INFO 140424560326464] Final configuration: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': '27', 'learning_rate': '0.001', 'lr_scheduler_factor': 0.1, 'optimizer': 'adam', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,32,32', 'precision_dtype': 'float32', 'augmentation_type': 'crop_color_transform', 'early_stopping': 'True', 'num_classes': '2', 'num_training_samples': '1000'} [08/23/2024 14:13:25 INFO 140424560326464] Searching for .lst files in /opt/ml/input/data/train_lst. [08/23/2024 14:13:25 INFO 140424560326464] Creating record files for train.lst [08/23/2024 14:13:25 INFO 140424560326464] Done creating record files... [08/23/2024 14:13:25 INFO 140424560326464] Searching for .lst files in /opt/ml/input/data/validation_lst. [08/23/2024 14:13:25 INFO 140424560326464] Creating record files for test.lst [08/23/2024 14:13:25 INFO 140424560326464] Done creating record files... [08/23/2024 14:13:25 INFO 140424560326464] use_pretrained_model: 0 [08/23/2024 14:13:25 INFO 140424560326464] multi_label: 0 [08/23/2024 14:13:25 INFO 140424560326464] Performing random weight initialization [08/23/2024 14:13:25 INFO 140424560326464] ---- Parameters ---- [08/23/2024 14:13:25 INFO 140424560326464] num_layers: 152 [08/23/2024 14:13:25 INFO 140424560326464] data type: <class 'numpy.float32'> [08/23/2024 14:13:25 INFO 140424560326464] epochs: 27 [08/23/2024 14:13:25 INFO 140424560326464] optimizer: adam [08/23/2024 14:13:25 INFO 140424560326464] beta_1: 0.9 [08/23/2024 14:13:25 INFO 140424560326464] beta_2: 0.999 [08/23/2024 14:13:25 INFO 140424560326464] eps: 1e-08 [08/23/2024 14:13:25 INFO 140424560326464] learning_rate: 0.001 [08/23/2024 14:13:25 INFO 140424560326464] num_training_samples: 1000 [08/23/2024 14:13:25 INFO 140424560326464] mini_batch_size: 32 [08/23/2024 14:13:25 INFO 140424560326464] image_shape: 3,32,32 [08/23/2024 14:13:25 INFO 140424560326464] num_classes: 2 [08/23/2024 14:13:25 INFO 140424560326464] augmentation_type: crop_color_transform [08/23/2024 14:13:25 INFO 140424560326464] kv_store: device [08/23/2024 14:13:25 INFO 140424560326464] checkpoint_frequency not set, will store the best model [08/23/2024 14:13:25 INFO 140424560326464] Using early stopping for training [08/23/2024 14:13:25 INFO 140424560326464] Early stopping minimum epochs: 10 [08/23/2024 14:13:25 INFO 140424560326464] Early stopping patience: 10 [08/23/2024 14:13:25 INFO 140424560326464] Early stopping tolerance: 0.01 [08/23/2024 14:13:25 INFO 140424560326464] -------------------- /opt/amazon/python3.8/lib/python3.8/subprocess.py:848: RuntimeWarning: line buffering (buffering=1) isn't supported in binary mode, the default buffer size will be used self.stdout = io.open(c2pread, 'rb', bufsize) [08/23/2024 14:13:26 INFO 140424560326464] Setting number of threads: 7 [14:13:30] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_11.1.x.379.0/AL2_x86_64/generic-flavor/src/src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable) [08/23/2024 14:13:35 INFO 140424560326464] Epoch[0] Batch [20]#011Speed: 115.674 samples/sec#011accuracy=0.619048 [08/23/2024 14:13:37 INFO 140424560326464] Epoch[0] Train-accuracy=0.663306 [08/23/2024 14:13:37 INFO 140424560326464] Epoch[0] Time cost=7.310 [08/23/2024 14:13:37 INFO 140424560326464] Epoch[0] Validation-accuracy=0.458333 [08/23/2024 14:13:38 INFO 140424560326464] Storing the best model with validation accuracy: 0.458333 [08/23/2024 14:13:38 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0001.params" [08/23/2024 14:13:42 INFO 140424560326464] Epoch[1] Batch [20]#011Speed: 173.216 samples/sec#011accuracy=0.739583 [08/23/2024 14:13:44 INFO 140424560326464] Epoch[1] Train-accuracy=0.745968 [08/23/2024 14:13:44 INFO 140424560326464] Epoch[1] Time cost=5.481 [08/23/2024 14:13:44 INFO 140424560326464] Epoch[1] Validation-accuracy=0.739583 [08/23/2024 14:13:44 INFO 140424560326464] Storing the best model with validation accuracy: 0.739583 [08/23/2024 14:13:45 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0002.params" [08/23/2024 14:13:48 INFO 140424560326464] Epoch[2] Batch [20]#011Speed: 176.741 samples/sec#011accuracy=0.752976 [08/23/2024 14:13:50 INFO 140424560326464] Epoch[2] Train-accuracy=0.744960 [08/23/2024 14:13:50 INFO 140424560326464] Epoch[2] Time cost=5.385 [08/23/2024 14:13:51 INFO 140424560326464] Epoch[2] Validation-accuracy=0.739583 [08/23/2024 14:13:55 INFO 140424560326464] Epoch[3] Batch [20]#011Speed: 175.092 samples/sec#011accuracy=0.778274 [08/23/2024 14:13:57 INFO 140424560326464] Epoch[3] Train-accuracy=0.785282 [08/23/2024 14:13:57 INFO 140424560326464] Epoch[3] Time cost=5.446 [08/23/2024 14:13:57 INFO 140424560326464] Epoch[3] Validation-accuracy=0.718750 [08/23/2024 14:14:01 INFO 140424560326464] Epoch[4] Batch [20]#011Speed: 174.402 samples/sec#011accuracy=0.796131 [08/23/2024 14:14:03 INFO 140424560326464] Epoch[4] Train-accuracy=0.796371 [08/23/2024 14:14:03 INFO 140424560326464] Epoch[4] Time cost=5.507 [08/23/2024 14:14:04 INFO 140424560326464] Epoch[4] Validation-accuracy=0.807292 [08/23/2024 14:14:04 INFO 140424560326464] Storing the best model with validation accuracy: 0.807292 [08/23/2024 14:14:04 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0005.params" [08/23/2024 14:14:08 INFO 140424560326464] Epoch[5] Batch [20]#011Speed: 176.411 samples/sec#011accuracy=0.816964 [08/23/2024 14:14:10 INFO 140424560326464] Epoch[5] Train-accuracy=0.814516 [08/23/2024 14:14:10 INFO 140424560326464] Epoch[5] Time cost=5.412 [08/23/2024 14:14:10 INFO 140424560326464] Epoch[5] Validation-accuracy=0.869792 [08/23/2024 14:14:10 INFO 140424560326464] Storing the best model with validation accuracy: 0.869792 [08/23/2024 14:14:11 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0006.params" [08/23/2024 14:14:14 INFO 140424560326464] Epoch[6] Batch [20]#011Speed: 175.944 samples/sec#011accuracy=0.806548 [08/23/2024 14:14:16 INFO 140424560326464] Epoch[6] Train-accuracy=0.811492 [08/23/2024 14:14:16 INFO 140424560326464] Epoch[6] Time cost=5.417 [08/23/2024 14:14:17 INFO 140424560326464] Epoch[6] Validation-accuracy=0.791667 [08/23/2024 14:14:21 INFO 140424560326464] Epoch[7] Batch [20]#011Speed: 175.821 samples/sec#011accuracy=0.846726 [08/23/2024 14:14:23 INFO 140424560326464] Epoch[7] Train-accuracy=0.849798 [08/23/2024 14:14:23 INFO 140424560326464] Epoch[7] Time cost=5.423 [08/23/2024 14:14:23 INFO 140424560326464] Epoch[7] Validation-accuracy=0.875000 [08/23/2024 14:14:23 INFO 140424560326464] Storing the best model with validation accuracy: 0.875000 [08/23/2024 14:14:24 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0008.params" [08/23/2024 14:14:28 INFO 140424560326464] Epoch[8] Batch [20]#011Speed: 174.745 samples/sec#011accuracy=0.846726 [08/23/2024 14:14:29 INFO 140424560326464] Epoch[8] Train-accuracy=0.844758 [08/23/2024 14:14:29 INFO 140424560326464] Epoch[8] Time cost=5.437 [08/23/2024 14:14:30 INFO 140424560326464] Epoch[8] Validation-accuracy=0.781250 [08/23/2024 14:14:34 INFO 140424560326464] Epoch[9] Batch [20]#011Speed: 176.324 samples/sec#011accuracy=0.821429 [08/23/2024 14:14:36 INFO 140424560326464] Epoch[9] Train-accuracy=0.811492 [08/23/2024 14:14:36 INFO 140424560326464] Epoch[9] Time cost=5.420 [08/23/2024 14:14:36 INFO 140424560326464] Epoch[9] Validation-accuracy=0.781250 [08/23/2024 14:14:40 INFO 140424560326464] Epoch[10] Batch [20]#011Speed: 175.927 samples/sec#011accuracy=0.836310 [08/23/2024 14:14:42 INFO 140424560326464] Epoch[10] Train-accuracy=0.830645 [08/23/2024 14:14:42 INFO 140424560326464] Epoch[10] Time cost=5.416 [08/23/2024 14:14:43 INFO 140424560326464] Epoch[10] Validation-accuracy=0.854167 [08/23/2024 14:14:47 INFO 140424560326464] Epoch[11] Batch [20]#011Speed: 176.771 samples/sec#011accuracy=0.857143 [08/23/2024 14:14:48 INFO 140424560326464] Epoch[11] Train-accuracy=0.840726 [08/23/2024 14:14:48 INFO 140424560326464] Epoch[11] Time cost=5.411 [08/23/2024 14:14:49 INFO 140424560326464] Epoch[11] Validation-accuracy=0.848214 [08/23/2024 14:14:53 INFO 140424560326464] Epoch[12] Batch [20]#011Speed: 174.549 samples/sec#011accuracy=0.836310 [08/23/2024 14:14:55 INFO 140424560326464] Epoch[12] Train-accuracy=0.849798 [08/23/2024 14:14:55 INFO 140424560326464] Epoch[12] Time cost=5.451 [08/23/2024 14:14:55 INFO 140424560326464] Epoch[12] Validation-accuracy=0.854167 [08/23/2024 14:15:00 INFO 140424560326464] Epoch[13] Batch [20]#011Speed: 175.955 samples/sec#011accuracy=0.877976 [08/23/2024 14:15:01 INFO 140424560326464] Epoch[13] Train-accuracy=0.864919 [08/23/2024 14:15:01 INFO 140424560326464] Epoch[13] Time cost=5.431 [08/23/2024 14:15:02 INFO 140424560326464] Epoch[13] Validation-accuracy=0.812500 [08/23/2024 14:15:06 INFO 140424560326464] Epoch[14] Batch [20]#011Speed: 172.709 samples/sec#011accuracy=0.875000 [08/23/2024 14:15:08 INFO 140424560326464] Epoch[14] Train-accuracy=0.877016 [08/23/2024 14:15:08 INFO 140424560326464] Epoch[14] Time cost=5.487 [08/23/2024 14:15:08 INFO 140424560326464] Epoch[14] Validation-accuracy=0.838542 [08/23/2024 14:15:13 INFO 140424560326464] Epoch[15] Batch [20]#011Speed: 175.278 samples/sec#011accuracy=0.854167 [08/23/2024 14:15:14 INFO 140424560326464] Epoch[15] Train-accuracy=0.839718 [08/23/2024 14:15:14 INFO 140424560326464] Epoch[15] Time cost=5.433 [08/23/2024 14:15:15 INFO 140424560326464] Epoch[15] Validation-accuracy=0.763393 [08/23/2024 14:15:19 INFO 140424560326464] Epoch[16] Batch [20]#011Speed: 174.455 samples/sec#011accuracy=0.858631 [08/23/2024 14:15:21 INFO 140424560326464] Epoch[16] Train-accuracy=0.847782 [08/23/2024 14:15:21 INFO 140424560326464] Epoch[16] Time cost=5.461 [08/23/2024 14:15:21 INFO 140424560326464] Epoch[16] Validation-accuracy=0.901042 [08/23/2024 14:15:22 INFO 140424560326464] Storing the best model with validation accuracy: 0.901042 [08/23/2024 14:15:22 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0017.params" [08/23/2024 14:15:26 INFO 140424560326464] Epoch[17] Batch [20]#011Speed: 178.527 samples/sec#011accuracy=0.858631 [08/23/2024 14:15:27 INFO 140424560326464] Epoch[17] Train-accuracy=0.870968 [08/23/2024 14:15:27 INFO 140424560326464] Epoch[17] Time cost=5.341 [08/23/2024 14:15:28 INFO 140424560326464] Epoch[17] Validation-accuracy=0.885417 [08/23/2024 14:15:32 INFO 140424560326464] Epoch[18] Batch [20]#011Speed: 177.334 samples/sec#011accuracy=0.867560 [08/23/2024 14:15:34 INFO 140424560326464] Epoch[18] Train-accuracy=0.854839 [08/23/2024 14:15:34 INFO 140424560326464] Epoch[18] Time cost=5.363 [08/23/2024 14:15:34 INFO 140424560326464] Epoch[18] Validation-accuracy=0.854167 [08/23/2024 14:15:38 INFO 140424560326464] Epoch[19] Batch [20]#011Speed: 177.343 samples/sec#011accuracy=0.875000 [08/23/2024 14:15:40 INFO 140424560326464] Epoch[19] Train-accuracy=0.855847 [08/23/2024 14:15:40 INFO 140424560326464] Epoch[19] Time cost=5.381 [08/23/2024 14:15:41 INFO 140424560326464] Epoch[19] Validation-accuracy=0.629464 [08/23/2024 14:15:45 INFO 140424560326464] Epoch[20] Batch [20]#011Speed: 176.046 samples/sec#011accuracy=0.873512 [08/23/2024 14:15:46 INFO 140424560326464] Epoch[20] Train-accuracy=0.854839 [08/23/2024 14:15:46 INFO 140424560326464] Epoch[20] Time cost=5.420 [08/23/2024 14:15:47 INFO 140424560326464] Epoch[20] Validation-accuracy=0.880208 [08/23/2024 14:15:51 INFO 140424560326464] Epoch[21] Batch [20]#011Speed: 178.341 samples/sec#011accuracy=0.857143 [08/23/2024 14:15:53 INFO 140424560326464] Epoch[21] Train-accuracy=0.866935 [08/23/2024 14:15:53 INFO 140424560326464] Epoch[21] Time cost=5.371 [08/23/2024 14:15:53 INFO 140424560326464] Epoch[21] Validation-accuracy=0.890625 [08/23/2024 14:15:57 INFO 140424560326464] Epoch[22] Batch [20]#011Speed: 177.657 samples/sec#011accuracy=0.900298 [08/23/2024 14:15:59 INFO 140424560326464] Epoch[22] Train-accuracy=0.898185 [08/23/2024 14:15:59 INFO 140424560326464] Epoch[22] Time cost=5.383 [08/23/2024 14:16:00 INFO 140424560326464] Epoch[22] Validation-accuracy=0.848958 [08/23/2024 14:16:04 INFO 140424560326464] Epoch[23] Batch [20]#011Speed: 173.202 samples/sec#011accuracy=0.888393 [08/23/2024 14:16:06 INFO 140424560326464] Epoch[23] Train-accuracy=0.886089 [08/23/2024 14:16:06 INFO 140424560326464] Epoch[23] Time cost=5.476 [08/23/2024 14:16:06 INFO 140424560326464] Epoch[23] Validation-accuracy=0.714286 [08/23/2024 14:16:10 INFO 140424560326464] Epoch[24] Batch [20]#011Speed: 176.612 samples/sec#011accuracy=0.910714 [08/23/2024 14:16:12 INFO 140424560326464] Epoch[24] Train-accuracy=0.906250 [08/23/2024 14:16:12 INFO 140424560326464] Epoch[24] Time cost=5.377 [08/23/2024 14:16:12 INFO 140424560326464] Epoch[24] Validation-accuracy=0.916667 [08/23/2024 14:16:13 INFO 140424560326464] Storing the best model with validation accuracy: 0.916667 [08/23/2024 14:16:13 INFO 140424560326464] Saved checkpoint to "/opt/ml/model/image-classification-0025.params" [08/23/2024 14:16:17 INFO 140424560326464] Epoch[25] Batch [20]#011Speed: 178.218 samples/sec#011accuracy=0.886905 [08/23/2024 14:16:18 INFO 140424560326464] Epoch[25] Train-accuracy=0.891129 [08/23/2024 14:16:18 INFO 140424560326464] Epoch[25] Time cost=5.375 [08/23/2024 14:16:19 INFO 140424560326464] Epoch[25] Validation-accuracy=0.864583 [08/23/2024 14:16:23 INFO 140424560326464] Epoch[26] Batch [20]#011Speed: 177.933 samples/sec#011accuracy=0.885417 [08/23/2024 14:16:25 INFO 140424560326464] Epoch[26] Train-accuracy=0.886089 [08/23/2024 14:16:25 INFO 140424560326464] Epoch[26] Time cost=5.349 [08/23/2024 14:16:25 INFO 140424560326464] Epoch[26] Validation-accuracy=0.906250 2024-08-23 14:16:48 Uploading - Uploading generated training model 2024-08-23 14:16:48 Completed - Training job completed Training seconds: 517 Billable seconds: 517
If all goes well, you'll end up with a model topping out above .8 validation accuracy. With only 1000 training samples in the CIFAR dataset, that's pretty good. We could definitely pursue data augmentation & gathering more samples to help us improve further, but for now let's proceed to deploy our model.
To begin with, let's configure Model Monitor to track our deployment. We'll define a DataCaptureConfig below:
from sagemaker.model_monitor import DataCaptureConfig
data_capture_config = DataCaptureConfig(
enable_capture=True, # Enable data capture
destination_s3_uri=f"s3://{bucket}/data_capture", # S3 bucket to store captured data
)
Note the destination_s3_uri parameter: At the end of the project, we can explore the data_capture directory in S3 to find crucial data about the inputs and outputs Model Monitor has observed on our model endpoint over time.
With that done, deploy your model on a single ml.m5.xlarge instance with the data capture config attached:
from sagemaker import Session
from botocore.exceptions import ClientError
# Initialize a session
sagemaker_session = Session()
# Attempt to delete the existing endpoint configuration if it exists
try:
sagemaker_session.delete_endpoint_config(endpoint_config_name='image-classification-endpoint')
print("Existing endpoint configuration deleted successfully.")
except ClientError as e:
if e.response['Error']['Code'] == 'ValidationException':
print("Endpoint configuration does not exist or has already been deleted.")
else:
raise e
# Proceed with deploying the new endpoint
deployment = img_classifier_model.deploy(
instance_type='ml.m5.large',
initial_instance_count=1,
endpoint_name='image-classification-endpoint-new',
data_capture_config=data_capture_config
)
endpoint = deployment.endpoint_name
INFO:sagemaker:Deleting endpoint configuration with name: image-classification-endpoint INFO:sagemaker:Creating model with name: image-classification-2024-08-23-14-31-52-598
Endpoint configuration does not exist or has already been deleted.
INFO:sagemaker:Creating endpoint-config with name image-classification-endpoint-new INFO:sagemaker:Creating endpoint with name image-classification-endpoint-new
---------------!
print(endpoint)
image-classification-endpoint-new
Note the endpoint name for later as well.
Next, instantiate a Predictor:
from sagemaker.predictor import Predictor
predictor = Predictor("image-classification-endpoint-new")
In the code snippet below we are going to prepare one of your saved images for prediction. Use the predictor to process the payload.
from sagemaker.serializers import IdentitySerializer
import base64
predictor.serializer = IdentitySerializer("image/png")
with open("./test/bicycle_s_001789.png", "rb") as f:
payload = f.read()
inference = predictor.predict(payload)
print(inference)
b'[0.3863007128238678, 0.6136993169784546]'
Your inference object is an array of two values, the predicted probability value for each of your classes (bicycle and motorcycle respectively.) So, for example, a value of b'[0.91, 0.09]' indicates the probability of being a bike is 91% and being a motorcycle is 9%.
print(inference)
b'[0.3863007128238678, 0.6136993169784546]'
predictor.delete_endpoint()
INFO:sagemaker:Deleting endpoint configuration with name: image-classification-endpoint-new INFO:sagemaker:Deleting endpoint with name: image-classification-endpoint-new
Your operations team uses Step Functions to orchestrate serverless workflows. One of the nice things about Step Functions is that workflows can call other workflows, so the team can easily plug your workflow into the broader production architecture for Scones Unlimited.
In this next stage you're going to write and deploy three Lambda functions, and then use the Step Functions visual editor to chain them together! Our functions are going to work with a simple data object:
{
"s3_bucket": "", # Source data S3 bucket
"s3_key": "", # Source data S3 key
"image_data": "", # base64 encoded string containing the image data
"inferences": [] # Output of predictor.predict
}
A good test object that you can use for Lambda tests and Step Function executions, throughout the next section, might look like this:
{
"s3_bucket": MY_BUCKET_NAME, # Fill in with your bucket
"s3_key": "test/bicycle_s_000513.png",
"image_data": ""
}
Using these fields, your functions can read and write the necessary data to execute your workflow. Let's start with the first function. Your first Lambda function will copy an object from S3, base64 encode it, and then return it to the step function as image_data in an event.
Go to the Lambda dashboard and create a new Lambda function with a descriptive name like "serializeImageData" and select thr 'Python 3.9' runtime. Add the same permissions as the SageMaker role you created earlier. (Reminder: you do this in the Configuration tab under "Permissions"). Once you're ready, use the starter code below to craft your Lambda handler:
import json
import boto3
import base64
s3 = boto3.client('s3')
def lambda_handler(event, context):
"""A function to serialize target data from S3"""
# Get the s3 address from the Step Function event input
key = ## TODO: fill in
bucket = ## TODO: fill in
# Download the data from s3 to /tmp/image.png
## TODO: fill in
# We read the data from a file
with open("/tmp/image.png", "rb") as f:
image_data = base64.b64encode(f.read())
# Pass the data back to the Step Function
print("Event:", event.keys())
return {
'statusCode': 200,
'body': {
"s3_bucket": bucket,
"s3_key": key,
"image_data": image_data,
"inferences": []
}
}
The next function is responsible for the classification part - we're going to take the image output from the previous function, decode it, and then pass inferences back to the the Step Function.
Because this Lambda will have runtime dependencies (i.e. the SageMaker SDK) you'll need to package them in your function. Key reading: https://docs.aws.amazon.com/lambda/latest/dg/python-package-create.html#python-package-create-with-dependency
Alternatively, you can package your dependencies in a lambda layer.
Create a new Lambda function with the same permissions and a descriptive name, then fill in the starter code below for your classifier Lambda.
import json
import sagemaker
import base64
from sagemaker.serializers import IdentitySerializer
# Fill this in with the name of your deployed model
ENDPOINT = ## TODO: fill in
def lambda_handler(event, context):
# Decode the image data
image = base64.b64decode(## TODO: fill in)
# Instantiate a Predictor
predictor = ## TODO: fill in
# For this model the IdentitySerializer needs to be "image/png"
predictor.serializer = IdentitySerializer("image/png")
# Make a prediction:
inferences = ## TODO: fill in
# We return the data back to the Step Function
event["body"]["inferences"] = json.loads(inferences)
return {
'statusCode': 200,
'body': event["body"]
}
Finally, we need to filter low-confidence inferences. Define a threshold between 1.00 and 0.000 for your model: what is reasonble for you? If the model predicts at .70 for it's highest confidence label, do we want to pass that inference along to downstream systems? Make one last Lambda function and tee up the same permissions:
import json
THRESHOLD = .93
def lambda_handler(event, context):
# Grab the inferences from the event
inferences = ## TODO: fill in
# Check if any values in our inferences are above THRESHOLD
meets_threshold = ## TODO: fill in
# If our threshold is met, pass our data back out of the
# Step Function, else, end the Step Function with an error
if meets_threshold:
pass
else:
raise(Exception("THRESHOLD_CONFIDENCE_NOT_MET"))
return {
'statusCode': 200,
'body': event["body"]
}
Once you have tested the lambda functions, save the code for each lambda function in a python script called 'lambda.py'.
With your lambdas in place, you can use the Step Functions visual editor to construct a workflow that chains them together. In the Step Functions console you'll have the option to author a Standard step function Visually.
When the visual editor opens, you'll have many options to add transitions in your workflow. We're going to keep it simple and have just one: to invoke Lambda functions. Add three of them chained together. For each one, you'll be able to select the Lambda functions you just created in the proper order, filter inputs and outputs, and give them descriptive names.
Make sure that you:
$.body)Take a screenshot of your working step function in action and export the step function as JSON for your submission package.
Great! Now you can use the files in ./test as test files for our workflow. Depending on our threshold, our workflow should reliably pass predictions about images from ./test on to downstream systems, while erroring out for inferences below our confidence threshold!
Do several step function invokations using data from the ./test folder. This process should give you confidence that the workflow both succeeds AND fails as expected. In addition, SageMaker Model Monitor will generate recordings of your data and inferences which we can visualize.
Here's a function that can help you generate test inputs for your invokations:
import random
import boto3
import json
def generate_test_case():
# Setup s3 in boto3
s3 = boto3.resource('s3')
# Randomly pick from sfn or test folders in our bucket
objects = s3.Bucket(bucket).objects.filter(Prefix="test")
# Grab any random object key from that folder!
obj = random.choice([x.key for x in objects])
return json.dumps({
"image_data": "",
"s3_bucket": bucket,
"s3_key": obj
})
generate_test_case()
'{"image_data": "", "s3_bucket": "sagemaker-us-east-1-933188268477", "s3_key": "test/motorcycle_s_001955.png"}'
In the Step Function dashboard for your new function, you can create new executions and copy in the generated test cases. Do several executions so that you can generate data you can evaluate and visualize.
Once you've done several executions, let's visualize the record of our inferences. Pull in the JSONLines data from your inferences like so:
from sagemaker.s3 import S3Downloader
# In S3 your data will be saved to a datetime-aware path
# Find a path related to a datetime you're interested in
data_path = "s3://sagemaker-us-east-1-933188268477/data_capture/image-classification-endpoint-new/AllTraffic/2024/08/23/15/29-44-707-1c54920d-2a5c-4db2-8542-bee4df6fe169.jsonl"
S3Downloader.download(data_path, "captured_data")
# Feel free to repeat this multiple times and pull in more data
['captured_data/29-44-707-1c54920d-2a5c-4db2-8542-bee4df6fe169.jsonl']
The data are in JSONLines format, where multiple valid JSON objects are stacked on top of eachother in a single jsonl file. We'll import an open-source library, jsonlines that was purpose built for parsing this format.
!pip install jsonlines
import jsonlines
Collecting jsonlines Using cached jsonlines-4.0.0-py3-none-any.whl.metadata (1.6 kB) Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.10/site-packages (from jsonlines) (23.2.0) Using cached jsonlines-4.0.0-py3-none-any.whl (8.7 kB) Installing collected packages: jsonlines Successfully installed jsonlines-4.0.0 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Now we can extract the data from each of the source files:
import os
# List the file names we downloaded
file_handles = []
for root, subFolders, files in os.walk("./captured_data"):
if files:
file_handles += [root + '/' + file for file in files]
# Dump all the data into an array
json_data = []
for jsonl in file_handles:
with jsonlines.open(jsonl) as f:
for dict_line in f.iter():
json_data.append(dict_line)
The data should now be a list of dictionaries, with significant nesting. We'll give you an example of some code that grabs data out of the objects and visualizes it:
# Define how we'll get our data
def simple_getter(obj):
inferences = obj["captureData"]["endpointOutput"]["data"]
timestamp = obj["eventMetadata"]["inferenceTime"]
return json.loads(inferences), timestamp
simple_getter(json_data[0])
([0.9674510955810547, 0.03254891559481621], '2024-08-23T15:29:44Z')
Finally, here's an example of a visualization you can build with this data. In this last part, you will take some time and build your own - the captured data has the input images, the resulting inferences, and the timestamps.
# Change the threshold value to the same value you used before.
THRESHOLD = 0.9
data_points = []
for obj in json_data:
inference, timestamp = simple_getter(obj)
data_points.append((timestamp, max(inference)))
# Sort data by timestamp.
data_points.sort()
# Populate the data for the x and y axis
x = [p[0] for p in data_points]
y = [p[1] for p in data_points]
# Todo: here is an visualization example, take some time to build another visual that helps monitor the result
# Plot the data
plt.scatter(x, y, c=['r' if k < THRESHOLD else 'b' for k in y ])
plt.axhline(y=0.94, color='g', linestyle='--')
plt.ylim(bottom=min(y) - 0.02) # New Fady
# Add labels
plt.ylabel("Confidence")
plt.suptitle("Observed Recent Inferences", size=14)
plt.title("Pictured with confidence threshold for production use", size=10)
# Give it some pizzaz!
plt.style.use("Solarize_Light2")
plt.gcf().autofmt_xdate()
INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting. INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.
import plotly.graph_objects as go
# Threshold value
THRESHOLD = 0.93
# Extract and sort data points
data_points = []
for obj in json_data:
inference, timestamp = simple_getter(obj)
data_points.append((timestamp, max(inference)))
# Create a DataFrame for Plotly
df = pd.DataFrame(data_points, columns=['Timestamp', 'Confidence'])
df.sort_values(by='Timestamp', inplace=True)
# Create traces for the plot
trace1 = go.Scatter(
x=df['Timestamp'],
y=df['Confidence'],
mode='markers',
marker=dict(color=[ 'red' if k < THRESHOLD else 'blue' for k in df['Confidence']]),
name='Inferences'
)
trace2 = go.Scatter(
x=df['Timestamp'],
y=[THRESHOLD] * len(df),
mode='lines',
line=dict(color='green', dash='dash'),
name='Threshold'
)
# Create layout
layout = go.Layout(
title='Observed Recent Inferences',
xaxis=dict(title='Timestamp', tickformat='%Y-%m-%d %H:%M'),
yaxis=dict(title='Confidence', range=[0, 1.02]),
showlegend=True
)
# Create the figure
fig = go.Figure(data=[trace1, trace2], layout=layout)
# Show the plot
fig.show()
You've reached the end of the project. In this project you created an event-drivent ML workflow that can be incorporated into the Scones Unlimited production architecture. You used the SageMaker Estimator API to deploy your SageMaker Model and Endpoint, and you used AWS Lambda and Step Functions to orchestrate your ML workflow. Using SageMaker Model Monitor, you instrumented and observed your Endpoint, and at the end of the project you built a visualization to help stakeholders understand the performance of the Endpoint over time. If you're up for it, you can even go further with these stretch goals: